SPGISpeech: 5, 000 hours of transcribed financial audio for fully formatted end-to-end speech recognition

By Patrick K. O’Neill, Vitaly Lavrukhin, Somshubra Majumdar, Vahid Noroozi, Yuekai Zhang, Oleksii Kuchaiev, Jagadeesh Balam, Yuliya Dovzhenko, Keenan Freyberg, M. Shulman and 3 more. 2021

113 citations recorded by the source corpus.

In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models. This adds complexity and limits performance, as many formatting tasks benefit from semantic information present in the acoustic signal but absent in transcription. Here we propose a new STT task: end-to-end neural transcription with fully formatted text for target labels. We present baseline Conformer-based models trained on a corpus of 5,000 hours of professionally transcribed earnings calls, achieving a CER of 1.7. As a contribution to the STT research community, we release the corpus free for non-commercial use at https://datasets.kensho.com/datasets/scribe.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

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Citations in the corpus

GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio (2021, methodology): “Another work is SPGISpeech [10], a corpus released by Kensho Technologies.”
Lhotse: a speech data representation library for the modern deep learning ecosystem (2021, methodology): “000 hours of recorded speech freely available with the recent releases of LibriSpeech [13], Multilingual LibriSpeech [15], LibriLight [9], GigaSpeech [7], SPGISpeech [12], The People’s Speech [8], and others.”